Frequency tables and barcharts

Many variables take on a small number of values and we can digest their frequencies en masse.

There are 6551 packages in this dataset.

Variables in CRANpractices that are not covered above:

## [1] "package"          "license"          "links_to"        
## [4] "vignette_format"  "vignette_builder" "downloads"       
## [7] "links_from"

License

Downloads

## Warning in loop_apply(n, do.ply): Removed 2 rows containing non-finite
## values (stat_density).

## Warning in loop_apply(n, do.ply): Removed 1 rows containing non-finite
## values (stat_density).
## Warning in loop_apply(n, do.ply): Removed 1 rows containing non-finite
## values (stat_density).

## Warning in loop_apply(n, do.ply): Removed 1 rows containing non-finite
## values (stat_density).
## Warning in loop_apply(n, do.ply): Removed 1 rows containing non-finite
## values (stat_density).

## Warning in loop_apply(n, do.ply): Removed 1 rows containing non-finite
## values (stat_density).
## Warning in loop_apply(n, do.ply): Removed 1 rows containing non-finite
## values (stat_density).

## Source: local data frame [5 x 3]
## 
##   upstream_repo no_dld yes_dld
## 1     BitBucket      0      23
## 2         Email      0      13
## 3        GitHub      1     825
## 4    None/Other      1    5431
## 5       R-Forge      0     257

I find it hard to believe that organic human-driven downloads would hit essentially every single package on CRAN within a month. Are there automated systems that, e.g., download CRAN in its entirety as a matter of policy?

Vignettes

Variables I haven’t looked at yet

links_to, links_from